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dbolya / yolact

A simple, fully convolutional model for real-time instance segmentation.
MIT License
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instance-segmentation pytorch real-time realtime yolact

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You Only Look At CoefficienTs

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A simple, fully convolutional model for real-time instance segmentation. This is the code for our papers:

YOLACT++ (v1.2) released! (Changelog)

YOLACT++'s resnet50 model runs at 33.5 fps on a Titan Xp and achieves 34.1 mAP on COCO's test-dev (check out our journal paper here).

In order to use YOLACT++, make sure you compile the DCNv2 code. (See Installation)

For a real-time demo, check out our ICCV video:

IMAGE ALT TEXT HERE

Some examples from our YOLACT base model (33.5 fps on a Titan Xp and 29.8 mAP on COCO's test-dev):

Example 0

Example 1

Example 2

Installation

Evaluation

Here are our YOLACT models (released on April 5th, 2019) along with their FPS on a Titan Xp and mAP on test-dev:

Image Size Backbone FPS mAP Weights
550 Resnet50-FPN 42.5 28.2 yolact_resnet50_54_800000.pth Mirror
550 Darknet53-FPN 40.0 28.7 yolact_darknet53_54_800000.pth Mirror
550 Resnet101-FPN 33.5 29.8 yolact_base_54_800000.pth Mirror
700 Resnet101-FPN 23.6 31.2 yolact_im700_54_800000.pth Mirror

YOLACT++ models (released on December 16th, 2019):

Image Size Backbone FPS mAP Weights
550 Resnet50-FPN 33.5 34.1 yolact_plus_resnet50_54_800000.pth Mirror
550 Resnet101-FPN 27.3 34.6 yolact_plus_base_54_800000.pth Mirror

To evalute the model, put the corresponding weights file in the ./weights directory and run one of the following commands. The name of each config is everything before the numbers in the file name (e.g., yolact_base for yolact_base_54_800000.pth).

Quantitative Results on COCO

# Quantitatively evaluate a trained model on the entire validation set. Make sure you have COCO downloaded as above.
# This should get 29.92 validation mask mAP last time I checked.
python eval.py --trained_model=weights/yolact_base_54_800000.pth

# Output a COCOEval json to submit to the website or to use the run_coco_eval.py script.
# This command will create './results/bbox_detections.json' and './results/mask_detections.json' for detection and instance segmentation respectively.
python eval.py --trained_model=weights/yolact_base_54_800000.pth --output_coco_json

# You can run COCOEval on the files created in the previous command. The performance should match my implementation in eval.py.
python run_coco_eval.py

# To output a coco json file for test-dev, make sure you have test-dev downloaded from above and go
python eval.py --trained_model=weights/yolact_base_54_800000.pth --output_coco_json --dataset=coco2017_testdev_dataset

Qualitative Results on COCO

# Display qualitative results on COCO. From here on I'll use a confidence threshold of 0.15.
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --display

Benchmarking on COCO

# Run just the raw model on the first 1k images of the validation set
python eval.py --trained_model=weights/yolact_base_54_800000.pth --benchmark --max_images=1000

Images

# Display qualitative results on the specified image.
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --image=my_image.png

# Process an image and save it to another file.
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --image=input_image.png:output_image.png

# Process a whole folder of images.
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --images=path/to/input/folder:path/to/output/folder

Video

# Display a video in real-time. "--video_multiframe" will process that many frames at once for improved performance.
# If you want, use "--display_fps" to draw the FPS directly on the frame.
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=my_video.mp4

# Display a webcam feed in real-time. If you have multiple webcams pass the index of the webcam you want instead of 0.
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=0

# Process a video and save it to another file. This uses the same pipeline as the ones above now, so it's fast!
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=input_video.mp4:output_video.mp4

As you can tell, eval.py can do a ton of stuff. Run the --help command to see everything it can do.

python eval.py --help

Training

By default, we train on COCO. Make sure to download the entire dataset using the commands above.

Trains yolact_base_config with a batch_size of 5. For the 550px models, 1 batch takes up around 1.5 gigs of VRAM, so specify accordingly.

python train.py --config=yolact_base_config --batch_size=5

Resume training yolact_base with a specific weight file and start from the iteration specified in the weight file's name.

python train.py --config=yolact_base_config --resume=weights/yolact_base_10_32100.pth --start_iter=-1

Use the help option to see a description of all available command line arguments

python train.py --help


## Multi-GPU Support
YOLACT now supports multiple GPUs seamlessly during training:

 - Before running any of the scripts, run: `export CUDA_VISIBLE_DEVICES=[gpus]`
   - Where you should replace [gpus] with a comma separated list of the index of each GPU you want to use (e.g., 0,1,2,3).
   - You should still do this if only using 1 GPU.
   - You can check the indices of your GPUs with `nvidia-smi`.
 - Then, simply set the batch size to `8*num_gpus` with the training commands above. The training script will automatically scale the hyperparameters to the right values.
   - If you have memory to spare you can increase the batch size further, but keep it a multiple of the number of GPUs you're using.
   - If you want to allocate the images per GPU specific for different GPUs, you can use `--batch_alloc=[alloc]` where [alloc] is a comma seprated list containing the number of images on each GPU. This must sum to `batch_size`.

## Logging
YOLACT now logs training and validation information by default. You can disable this with `--no_log`. A guide on how to visualize these logs is coming soon, but now you can look at `LogVizualizer` in `utils/logger.py` for help.

## Pascal SBD
We also include a config for training on Pascal SBD annotations (for rapid experimentation or comparing with other methods). To train on Pascal SBD, proceed with the following steps:
 1. Download the dataset from [here](http://home.bharathh.info/pubs/codes/SBD/download.html). It's the first link in the top "Overview" section (and the file is called `benchmark.tgz`).
 2. Extract the dataset somewhere. In the dataset there should be a folder called `dataset/img`. Create the directory `./data/sbd` (where `.` is YOLACT's root) and copy `dataset/img` to `./data/sbd/img`.
 4. Download the COCO-style annotations from [here](https://drive.google.com/open?id=1ExrRSPVctHW8Nxrn0SofU1lVhK5Wn0_S).
 5. Extract the annotations into `./data/sbd/`.
 6. Now you can train using `--config=yolact_resnet50_pascal_config`. Check that config to see how to extend it to other models.

I will automate this all with a script soon, don't worry. Also, if you want the script I used to convert the annotations, I put it in `./scripts/convert_sbd.py`, but you'll have to check how it works to be able to use it because I don't actually remember at this point.

If you want to verify our results, you can download our `yolact_resnet50_pascal_config` weights from [here](https://drive.google.com/open?id=1yLVwtkRtNxyl0kxeMCtPXJsXFFyc_FHe). This model should get 72.3 mask AP_50 and 56.2 mask AP_70. Note that the "all" AP isn't the same as the "vol" AP reported in others papers for pascal (they use an averages of the thresholds from `0.1 - 0.9` in increments of `0.1` instead of what COCO uses).

## Custom Datasets
You can also train on your own dataset by following these steps:
 - Create a COCO-style Object Detection JSON annotation file for your dataset. The specification for this can be found [here](http://cocodataset.org/#format-data). Note that we don't use some fields, so the following may be omitted:
   - `info`
   - `liscense`
   - Under `image`: `license, flickr_url, coco_url, date_captured`
   - `categories` (we use our own format for categories, see below)
 - Create a definition for your dataset under `dataset_base` in `data/config.py` (see the comments in `dataset_base` for an explanation of each field):
```Python
my_custom_dataset = dataset_base.copy({
    'name': 'My Dataset',

    'train_images': 'path_to_training_images',
    'train_info':   'path_to_training_annotation',

    'valid_images': 'path_to_validation_images',
    'valid_info':   'path_to_validation_annotation',

    'has_gt': True,
    'class_names': ('my_class_id_1', 'my_class_id_2', 'my_class_id_3', ...)
})

Creating a Custom Dataset from Scratch

See this nice post by @Amit12690 for tips on how to annotate a custom dataset and prepare it for use with YOLACT.

Citation

If you use YOLACT or this code base in your work, please cite

@inproceedings{yolact-iccv2019,
  author    = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},
  title     = {YOLACT: {Real-time} Instance Segmentation},
  booktitle = {ICCV},
  year      = {2019},
}

For YOLACT++, please cite

@article{yolact-plus-tpami2020,
  author  = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title   = {YOLACT++: Better Real-time Instance Segmentation}, 
  year    = {2020},
}

Contact

For questions about our paper or code, please contact Daniel Bolya.